Inworld

Staff / Principal Machine Learning Engineer - Canada

Mountain View, California, United States
345 days ago

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Why Join Inworld

Inworld is the best-funded startup in AI and gaming with a $500 million valuation and backing from top tier investors like Intel, Microsoft, Lightspeed, Bitkraft, Founders Fund, Kleiner Perkins, and more. Inworld was recognized by CB Insights list of the 100 most promising AI companies in the world. We’ve also been nominated alongside Anthropic, DeepMind, OpenAI and Nvidia for the Generative AI Innovator of the Year at the VentureBeat Awards 2023, and are a Gartner Cool Vendor in 2023.

Inworld is the leading character engine for creating AI NPCs in games and immersive entertainment. Inworld powers NPCs in experiences built by Niantic, NetEase Games, LG, Alpine Electronics, the Disney Accelerator, and more. We go beyond large language models (LLMs) to add multimodal orchestration of personality and contextual awareness that renders NPCs within the lore and logic of their worlds. 

We are seeking Staff and Principal level Machine Learning Engineers with extensive experience in Natural Language Processing (NLP). You will be at the forefront of building generative AI products that utilize Large Language Models (LLMs) to create next-generation AI characters.

Qualifications

  • BA/BS degree or higher in Computer Science, Engineering, or a similar technical field.
  • 6 years of experience with software development in one or more programming languages 
  • 4 years of experience with applying machine learning algorithms in natural language processing domains.
  • 1+ years of experience training LLMs (6B parameters and larger) such as NeoX, LLaMA, PaLM, etc. is considered a big plus.
  • Deep knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX.   

Responsibilities

  • Explore and experiment with cutting edge ML techniques for applied NLP. 
  • Develop and test production-grade scalable generative machine learning models.

In-office location: Vancouver, Canada.

Remote location: Canada.

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